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Given the high prevalence of breast cancer and the diverse genetic backgrounds of patients, a growing body of research emphasizes the importance of pharmacogenetic-based pharmacokinetic analysis in optimizing treatment outcomes. The treatment of breast cancer involves multiple drugs whose metabolism and efficacy are influenced by individual genetic variations. Genetic polymorphisms in drug-metabolizing enzymes and transport proteins are crucial in the regulation of pharmacokinetics. Our review aims to investigate the opportunities and challenges of pharmacogenomic-based pharmacokinetic analysis as a precision medicine tool in breast cancer management. [ABSTRACT FROM AUTHOR]

Over 120 small-molecule kinase inhibitors (SMKIs) have been approved worldwide for treating various diseases, with nearly 70 FDA approvals specifically for cancer treatment, focusing on targets like the epidermal growth factor receptor (EGFR) family. Kinase-targeted strategies encompass monoclonal antibodies and their derivatives, such as nanobodies and peptides, along with innovative approaches like the use of kinase degraders and protein kinase interaction inhibitors, which have recently demonstrated clinical progress and potential in overcoming resistance. Nevertheless, kinase-targeted strategies encounter significant hurdles, including drug resistance, which greatly impacts the clinical benefits for cancer patients, as well as concerning toxicity when combined with immunotherapy, which restricts the full utilization of current treatment modalities. Despite these challenges, the development of kinase inhibitors remains highly promising. The extensively studied tyrosine kinase family has 70% of its targets in various stages of development, while 30% of the kinase family remains inadequately explored. Computational technologies play a vital role in accelerating the development of novel kinase inhibitors and repurposing existing drugs. Recent FDA-approved SMKIs underscore the importance of blood–brain barrier permeability for long-term patient benefits. This review provides a comprehensive summary of recent FDA-approved SMKIs based on their mechanisms of action and targets. We summarize the latest developments in potential new targets and explore emerging kinase inhibition strategies from a clinical perspective. Lastly, we outline current obstacles and future prospects in kinase inhibition. [ABSTRACT FROM AUTHOR]

Precise targeting has become the main direction of anti-cancer drug development. Trophoblast cell surface antigen 2 (Trop-2) is highly expressed in different solid tumors but rarely in normal tissues, rendering it an attractive target. Trop-2-targeted antibody-drug conjugates (ADCs) have displayed promising efficacy in treating diverse solid tumors, especially breast cancer and urothelial carcinoma. However, their clinical application is still limited by insufficient efficacy, excessive toxicity, and the lack of biological markers related to effectiveness. This review summarizes the clinical trials and combination therapy strategies for Trop-2-targeted ADCs, discusses the current challenges, and provides new insights for future advancements. [ABSTRACT FROM AUTHOR]

Background: Exosomal circular RNAs (circRNAs) played critical roles in tumor development and progression and might be novel biomarkers in the diagnosis and treatment of various cancers. However, the biological functions and clinical implications of exosomal circRNAs in breast cancer are unclear. Methods: Expression profiles of exosomal circRNAs the in exoRBase 2.0 database were used to identify differentially expressed exosomal circRNAs in breast cancer. The LASSO and SVM‐RFE algorithms followed by multivariate logistic regression analysis were performed to construct the diagnostic model. The target genes of circRNAs were selected by combing differential expression analysis and CSCD, TargetScan and ENCORI databases. Univariate and multivariate survival analysis were conducted to construct a survival‐associated exosomal circRNA‐miRNA‐mRNA network. GSVA and CIBERSORT algorithms were used to evaluate the cancer hallmarks and immune cells in breast cancer and Spearman correlation analysis was used to investigate their correlations with the circRNA‐miRNA‐mRNA network. Results: In total, 347 upregulated and three downregulated exosomal circRNAs were identified in breast cancer patients. The diagnostic model based on 14 exosomal circRNAs showed a high area under the curve (AUC) value in both the training (AUC = 0.98) and validation (AUC = 0.94) dataset. In total, 70 miRNAs and 1147 mRNAs were selected as the downstream targets of circRNAs and were revealed to participate in tumor‐associated pathways, including the PI3K‐AKT, MAPK, RAS and RAP1 pathways, as well as calcium signaling pathways, in addition to transcriptional misregulations. The constructed survival‐associated exosomal circRNA‐miRNA‐mRNA network contained nine exosomal circRNAs, 12 miRNAs and 10 mRNAs, and showed complicated correlations and interactions within networks. Cancer hallmark pathways, including the TGF‐β, KRAS and MYC signaling pathways, tumor angiogenesis, epithelial–mesenchymal transition, DNA repair and G2M checkpoint, as well as immune cells, including CD4+ and CD8+ T cells, dendritic cells, mast cells, macrophage cells, memory B cells and natural killer cells, were closely correlated with the circRNA‐miRNA‐mRNA network. Conclusions: The present study is the first to systematically analyze the exosomal circRNAs in breast cancer. We established an exosomal circRNA diagnostic model and constructed a survival‐associated exosomal circRNA‐miRNA‐mRNA network. Our results revealed the complicated functions and potential mechanisms of the exosomal circRNA‐miRNA‐mRNA network in breast cancer, which need to be validated further in future studies. [ABSTRACT FROM AUTHOR]